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Projeto de investigação
Instituto de Telecomunicações
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A novel approach to automatic seizure detection using computer vision and independent component analysis
Publication . Garção, Vicente; Abreu, Mariana; Peralta, Ana; Bentes, Carla; Fred, Ana; Silva, Hugo
Objective: Epilepsy is a neurological disease that affects ~50 million people worldwide, 30% of which have refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure-detection method that ensured unobtrusiveness and privacy preservation, and provided novel approaches to reduce confounds and increase reliability.
Methods: The proposed approach is a video-based seizure-detection method based on optical flow, principal component analysis, independent component analysis, and machine learning classification. This method was tested on a set of 21 tonic-clonic seizure videos (5-30 min each, total of 4 h and 36 min of recordings) from 12 patients using leave-one-subject-out cross-validation.
Results: High accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 s. When compared to annotations by health care professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 s.
Significance: The video-based seizure-detection method described herein is highly accurate. Moreover, it is intrinsically privacy preserving, due to the use of optical flow motion quantification. In addition, given our novel independence-based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.
Electromagnetic device for axillary Lymph Node diagnosis
Publication . Savazzi, Matteo; Conceição, Raquel Cruz da; Felício, João Manuel de Almeida Monteiro
The diagnosis of axillary lymph nodes (ALNs) is fundamental to determine breast cancer staging before making therapeutical decisions. Non-invasive medical imaging techniques are often used to diagnose ALNs, but they lack sensitivity and specificity. This thesis aims to contribute to the development of microwave imaging (MWI) prototype system to detect and diagnose ALNs.
The dielectric properties of freshly excised animal lymph nodes (LNs) and human ALNs are measured (0.5-8.5GHz) with the Open-Ended Coaxial-Probe technique. The results show that the relative permittivity of healthy ALNs ranges between 30 and 50 at 4.5GHz, which contrasts well with the surrounding fat tissue, potentially enabling ALN detection with MWI. Additionally, the effects of freezing and defrosting of biological tissue dielectric properties are studied, which is motivated by the possibility of measuring previously frozen and defrosted LNs. The results suggest that measuring defrosted tissues does not affect the estimation of their dielectric properties by more than 9% at 4.5GHz, paving the way to measure previously frozen LN.
The measured ALN dielectric properties are used to develop an anatomically realistic axillary phantom. The phantom derives from the segmentation of a thoracic computed-tomography scan, and it is made of polymeric containers filled with appropriate tissue mimicking liquids, representing fat and muscle. Finally, ALN microwave tomography is tested (0.5-2.5GHz) on the developed anthropomorphic phantom, using the distorted Born iterative method. The numerical results show that: (i) prior knowledge on the position of muscle tissue is fundamental for ALN detection; (ii) performing two-step measurements, with the antenna set in two different angular positions, can increase the amount of retrievable information, and enhance imaging results. Regarding experimental results, the proposed system can detect an ALN in different positions in the axillary region, which motivates further studies on ALN MWI.
Microwave Imaging to Improve Breast Cancer Diagnosis
Publication . Godinho, Daniela M.; Conceição, Raquel Cruz da; Fernandes, Carlos António Cardoso
Breast cancer is the most prevalent type of cancer worldwide. The correct diagnosis of Axillary Lymph Nodes (ALNs) is important for an accurate staging of breast cancer. The performance of current imaging modalities for both breast cancer detection and staging is still unsatisfactory. Microwave Imaging (MWI) has been studied to aid breast cancer diagnosis. This thesis addresses several novel aspects of the development of air-operated MWI systems for both breast cancer detection and staging.
Firstly, refraction effects in air-operated setups are evaluated to understand whether refraction calculation should be included in image reconstruction algorithms. Then, the research completed towards the development of a MWI system to detect the ALNs is presented. Anthropomorphic numerical phantoms of the axillary region are created, and the dielectric properties of ALNs are estimated from Magnetic Resonance Imaging exams. The first pre-clinical MWI setup tailored to detect ALNs is numerically and experimentally tested. To complement MWI results, the feasibility of using machine learning algorithms to classify healthy and metastasised ALNs using microwave signals is analysed. Finally, an additional study towards breast cancer detection is presented by proposing a prototype which uses a focal system to focus the energy into the breast and decrease the coupling between antennas.
The results show refraction calculation may be neglected in low to moderate permittivity media. Moreover, MWI has the potential as an imaging technique to assess ALN diagnosis as estimation of dielectric properties indicate there is sufficient contrast between healthy and metastasised ALNs, and the imaging results obtained in this thesis are promising for ALN detection. The performance of classification models shows these models may potentially give complementary information to imaging results. The proposed breast imaging prototype also shows promising results for breast cancer detection.
e-CoVig: a novel mHealth system for remote monitoring of symptoms in COVID-19
Publication . Raposo, Afonso; Marques, Luis; Correia, Rafael; Melo, Francisco; Valente, João; Pereira, Telmo; Rosario, Luis; Froes, Filipe; Sanches, João; Silva, Hugo Plácido da
In 2019, a new virus, SARS-CoV-2, responsible for the COVID-19 disease, was discovered. Asymptomatic and mildly symptomatic patients were forced to quarantine and closely monitor their symptoms and vital signs, most of the time at home. This paper describes e-CoVig, a novel mHealth application, developed as an alternative to the current monitoring paradigm, where the patients are followed up by direct phone contact. The e-CoVig provides a set of functionalities for remote reporting of symptoms, vital signs, and other clinical information to the health services taking care of these patients. The application is designed to register and transmit the heart rate, blood oxygen saturation (SpO2), body temperature, respiration, and cough. The system features a mobile application, a web/cloud platform, and a low-cost specific device to acquire the temperature and SpO2. The architecture of the system is flexible and can be configured for different operation conditions. Current commercial devices, such as oximeters and thermometers, can also be used and read using the optical character recognition (OCR) functionality of the system. The data acquired at the mobile application are sent automatically to the web/cloud application and made available in real-time to the medical staff, enabling the follow-up of several users simultaneously without the need for time consuming phone call interactions. The system was already tested for its feasibility and a preliminary deployment was performed on a nursing home showing promising results.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/50008/2020